Dianhui Wang

Artificial Intelligence · 2011–2017

A researcher in the Science corpus: 5 papers; trusts 53 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 70 citations into it; #9,152 of 35,345 by trust here, #10,566 by citations
• Computer Vision and Pattern Recognition — 25 citations into it; #8,697 of 21,575 by trust here, #14,478 by citations
• Electrical and Electronic Engineering — 25 citations into it; #4,636 of 16,539 by trust here, #7,308 by citations
• Optimization & Training — 25 citations into it
• Model Architectures — 22 citations into it
• Statistical Learning Theory — 21 citations into it
• Scaling & Learning Theory — 9 citations into it
• Computational Mechanics — 8 citations into it

Whom Dianhui Wang trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. G. Huang
2. Andrew R. Barron
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Leo Breiman
6. Robert E. Schapire
7. Kurt Hornik
8. Yoshua Bengio
9. Ali Rahimi
10. Yann LeCun

Who trusts Dianhui Wang overall

1. Le Zhang
2. Pei Wang
3. Laura Palagi
4. Ponnuthurai Nagaratnam Suganthan
5. Simone Scardapane
6. Seyedali Mirjalili
7. Yijun Li
8. Justin K. Pugh
9. Stefan Lee
10. Soujanya Poria

Papers from Dianhui Wang's perspective

Ordered by how strongly the researchers Dianhui Wang trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Extreme learning machines: a survey (2011) — 1,939 citations
• Fast decorrelated neural network ensembles with random weights (2014) — 194 citations
• Stochastic Configuration Networks: Fundamentals and Algorithms (2017) — 638 citations
• Randomness in neural networks: an overview (2017) — 343 citations
• Distributed learning for Random Vector Functional-Link networks (2015) — 148 citations